Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method, comprising: identifying, with a data processing system, a first workflow segment corresponding to a user-interface (UI) modality of an application, wherein the application is developed to run on a predetermined data processing platform; selecting, with the data processing system, at least one other workflow segment to transform the UI modality of the application, wherein the at least one other workflow segment is configured to perform, when executed on a different predetermined data processing platform, a function comparable to a function performable by the first workflow segment; and transforming, using the selected at least one other workflow segment, the UI modality of the application to perform, when executed on the different predetermined data processing platform, the function comparable to the function performable by the first workflow segment; wherein the at least one other workflow segment is selected from a plurality of alternative workflow segments that are semantically similar to the first workflow segment, and wherein the selecting is based on classifying the first workflow segment with a classification model trained using machine learning to map workflow segments and corresponding UI modalities of the application to different predetermined data processing platforms.
The invention relates to adapting user-interface (UI) modalities of applications across different data processing platforms. The problem addressed is the difficulty in maintaining consistent UI functionality when an application is ported from one platform to another, as UI workflows may not directly translate between platforms. The solution involves a method for transforming UI modalities by identifying a first workflow segment associated with a UI modality in an application designed for a specific platform. The system then selects at least one alternative workflow segment from a pool of semantically similar options, where the selected segment performs a comparable function on a different platform. The transformation ensures the UI modality retains its functionality when executed on the new platform. The selection process uses a machine learning-trained classification model that maps workflow segments and their corresponding UI modalities to different platforms, ensuring the chosen alternative is semantically equivalent. This approach automates the adaptation of UI workflows, reducing manual effort and improving cross-platform compatibility.
2. The method of claim 1 , further comprising generating a rank-ordered list of recommended workflow segments in response to selecting the at least one other workflow segment comprising a plurality of other workflow segments.
A system and method for optimizing workflow recommendations in a digital environment addresses the challenge of efficiently identifying and suggesting relevant workflow segments to users based on their interactions. The invention involves analyzing user behavior to determine preferences and patterns, then generating a ranked list of recommended workflow segments. When a user selects one or more workflow segments from a plurality of available options, the system processes these selections to refine the recommendations. The method includes tracking user interactions with workflow segments, such as viewing, editing, or executing them, to assess relevance and utility. Based on this data, the system generates a rank-ordered list of additional workflow segments that are likely to be useful to the user. The ranking may consider factors like frequency of use, user feedback, or contextual relevance. This approach enhances productivity by reducing the time users spend searching for appropriate workflow segments, while also improving the accuracy of recommendations over time through continuous learning from user behavior. The system may be applied in various domains, including software development, project management, or automated task execution, where workflow optimization is critical.
3. The method of claim 1 , further comprising transforming the UI modality of the application by automatically substituting a second code segment for a first code segment, wherein the first code segment corresponds to the first workflow segment, and wherein the second code segment corresponds to the at least one other workflow segment and performs, when executed on the different predetermined data processing platform, the function comparable to the function performable by the first workflow segment.
This invention relates to adapting software applications for different data processing platforms by transforming user interface (UI) modalities. The problem addressed is the difficulty of running applications designed for one platform on another due to differences in UI capabilities, such as touch vs. mouse input or screen size variations. The solution involves dynamically modifying the application's code to maintain functionality across platforms. The method includes identifying a first workflow segment of an application designed for a first platform and determining at least one other workflow segment compatible with a second platform. The UI modality is transformed by automatically replacing a first code segment (corresponding to the first workflow segment) with a second code segment (corresponding to the other workflow segment). The second code segment, when executed, performs a function comparable to the original workflow segment but is optimized for the second platform. This ensures the application retains its core functionality while adapting to the new platform's UI constraints. The transformation may involve changes to input handling, display rendering, or interaction logic to ensure seamless operation across diverse hardware and software environments. The approach enables applications to be ported or adapted with minimal manual intervention, reducing development time and cost.
4. The method of claim 1 , further comprising predicting a domain of the application during an in-development phase using a prediction model trained with machine learning to predict the domain of the application based on a predetermined set of application features.
This invention relates to software development, specifically to predicting the domain of an application during its development phase. The problem addressed is the lack of automated tools to identify the intended domain of an application early in development, which can aid in resource allocation, compliance checks, and targeted feature recommendations. The method involves using a machine learning model trained to predict the domain of an application based on a set of predefined application features. These features may include code structure, API usage, libraries imported, or other technical characteristics. The model is trained on historical data where applications are labeled with their respective domains. During development, the model analyzes the in-progress application and outputs a predicted domain, helping developers and stakeholders understand the application's likely use case. The prediction model is trained using supervised learning techniques, where input features are mapped to known domain labels. The model may be updated periodically with new data to improve accuracy. This approach enables early identification of the application's domain, allowing for better planning and optimization of development resources. The system can also flag potential compliance or security risks associated with the predicted domain.
5. The method of claim 1 , further comprising generating a training corpus for training the classification model by extracting workflow semantics and corresponding UI modalities of the application from each of a plurality of platform-specific versions of previously developed applications.
This invention relates to improving the training of classification models for application development by leveraging workflow semantics and user interface (UI) modalities from existing applications. The core problem addressed is the inefficiency in training models to understand and generate application workflows and UI elements, which is critical for automating or assisting in software development. The method involves creating a training corpus by analyzing multiple platform-specific versions of previously developed applications. From these applications, workflow semantics—such as the sequence of operations, user interactions, and logical flows—are extracted. Additionally, the corresponding UI modalities, including visual elements, input methods, and interaction patterns, are identified. This data is then used to train a classification model, enabling it to recognize and predict workflows and UI designs for new applications. By utilizing existing applications as a training source, the method reduces the need for manual data annotation and improves the model's ability to generalize across different platforms. The approach ensures that the trained model can accurately classify and generate application components based on learned patterns from real-world examples. This enhances the efficiency and accuracy of automated application development tools.
6. The method of claim 1 , further comprising generating the plurality of alternative workflow segments by clustering semantically similar workflow segments with a classification model trained using machine learning to cluster the workflow segments based on a predetermined distance metric.
This invention relates to workflow optimization in automated systems, addressing the challenge of efficiently generating and managing alternative workflow segments to improve process efficiency. The method involves analyzing workflow segments to identify and group semantically similar segments using a machine learning-based classification model. The model is trained to cluster these segments based on a predetermined distance metric, ensuring that similar workflow segments are grouped together. This clustering process helps in reducing redundancy, improving workflow consistency, and enabling more effective automation. The generated clusters of workflow segments can then be used to optimize workflow execution, enhance decision-making, and streamline process automation. The machine learning model is specifically trained to recognize patterns and similarities in workflow data, allowing for accurate and efficient clustering. This approach ensures that workflow segments are categorized based on their functional and semantic similarities, leading to more efficient and adaptable workflow management. The method supports dynamic adjustments in workflow execution, improving overall system performance and reducing manual intervention.
7. The method of claim 1 , further comprising determining the corresponding UI modalities of the application based on accessibility text of the plurality of alternative workflow segments.
This invention relates to improving accessibility in software applications by dynamically determining and applying user interface (UI) modalities based on accessibility text associated with alternative workflow segments. The technology addresses the challenge of ensuring applications are usable by individuals with diverse accessibility needs, such as those requiring screen readers, voice control, or other assistive technologies. The method involves analyzing an application's workflow, which consists of multiple segments representing different user interaction paths. Each segment is associated with accessibility text that describes its function or purpose. By parsing this text, the system identifies the appropriate UI modalities (e.g., visual, auditory, tactile) required for each segment. For example, a segment with text indicating a button's purpose may trigger a screen reader announcement, while another segment may enable voice commands for navigation. The system dynamically adjusts the UI modalities in real-time as the user progresses through the workflow, ensuring consistent accessibility support. This approach eliminates the need for manual configuration or static accessibility settings, adapting instead to the context of each interaction. The method also supports multi-modal interactions, allowing users to switch between different input/output methods seamlessly. By leveraging accessibility text, the invention ensures that applications remain inclusive without requiring extensive developer intervention, reducing development costs and improving user experience for individuals with disabilities. The solution is particularly useful in complex applications with multiple workflow paths, where traditional accessibility features may not cover all scenarios.
8. A system, comprising: computer hardware having at least one processor programmed to initiate executable operations, the executable operations including: identifying a first workflow segment corresponding to a user-interface (UI) modality of an application, wherein the application is developed to run on a predetermined data processing platform; selecting at least one other workflow segment to transform the UI modality of the application, wherein the at least one other workflow segment is configured to perform, when executed on a different predetermined data processing platform, a function comparable to a function performable by the first workflow segment; and transforming, using the selected at least one other workflow segment, the UI modality of the application to perform, when executed on the different predetermined data processing platform, the function comparable to the function performable by the first workflow segment; wherein the at least one other workflow segment is selected from a plurality of alternative workflow segments that are semantically similar to the first workflow segment, and wherein the selecting is based on classifying the first workflow segment with a classification model trained using machine learning to map workflow segments and corresponding UI modalities of the application to different predetermined data processing platforms.
The system addresses the challenge of adapting applications designed for one data processing platform to function on different platforms while maintaining comparable user interface (UI) functionality. Applications often require significant redesign when ported to new platforms, leading to inefficiencies and inconsistencies in user experience. This system automates the transformation of UI modalities by leveraging workflow segments and machine learning. The system includes computer hardware with at least one processor programmed to perform specific operations. First, it identifies a workflow segment corresponding to a UI modality in an application designed for a specific platform. Next, it selects alternative workflow segments that can perform similar functions on a different platform. These segments are chosen from a pool of semantically similar options using a classification model trained via machine learning. The model maps workflow segments and their UI modalities to different platforms, ensuring the selected segment maintains comparable functionality. Finally, the system transforms the original UI modality using the selected segment, enabling the application to operate effectively on the new platform. This approach reduces manual redesign efforts and ensures consistent UI behavior across platforms by leveraging automated semantic analysis and machine learning-driven segmentation.
9. The system of claim 8 , wherein the executable operations further include generating a rank-ordered list of recommended workflow segments in response to selecting the at least one other workflow segment comprising a plurality of other workflow segments.
This invention relates to workflow management systems that recommend workflow segments to users. The problem addressed is the inefficiency in manually identifying and selecting relevant workflow segments from a large set of available options, which can slow down productivity and decision-making. The system improves upon prior workflow recommendation systems by dynamically generating a rank-ordered list of recommended workflow segments based on user selections. When a user selects at least one workflow segment from a plurality of available segments, the system analyzes the selected segment and other related segments to determine the most relevant recommendations. The ranking is based on factors such as similarity, relevance, or historical usage patterns, ensuring that the most appropriate segments are prioritized. This dynamic ranking helps users quickly identify the next best steps in a workflow, reducing the time and effort required to navigate complex processes. The system may also incorporate user preferences, contextual data, or machine learning models to refine recommendations over time. By automating the recommendation process and presenting options in a prioritized manner, the system enhances workflow efficiency and user experience.
10. The system of claim 8 , wherein the executable operations further include transforming the UI modality of the application by automatically substituting a second code segment for a first code segment, wherein the first code segment corresponds to the first workflow segment, and wherein the second code segment corresponds to the at least one other workflow segment and performs, when executed on the different predetermined data processing platform, the function comparable to the function performable by the first workflow segment.
This invention relates to a system for adapting user interface (UI) modalities in applications across different data processing platforms. The problem addressed is the difficulty of maintaining consistent UI functionality when an application is deployed on diverse platforms with varying capabilities, such as different operating systems, devices, or input/output methods. The system dynamically transforms the UI modality by substituting code segments to ensure compatibility and functionality across platforms. The system includes executable operations that analyze an application's workflow, which is divided into segments. When the application is deployed on a different platform, the system identifies a first workflow segment that is incompatible with the new platform. Instead of executing the original code for this segment, the system automatically replaces it with a second code segment. The second code segment corresponds to an alternative workflow segment that performs the same function as the original but is optimized for the new platform. This substitution ensures that the application's functionality remains intact despite platform differences. The system may also include a mapping mechanism to associate workflow segments with their compatible alternatives, allowing seamless adaptation without manual intervention. The approach enables applications to maintain usability and performance across heterogeneous environments.
11. The system of claim 8 , wherein the executable operations further include predicting a domain of the application during an in-development phase using a prediction model trained with machine learning to predict the domain of the application based on a predetermined set of application features.
A system for application development includes a prediction model trained with machine learning to identify the domain of an application during its development phase. The model analyzes a predetermined set of application features to predict the domain, enabling early categorization and optimization of the application. The system may also include components for generating a domain-specific development environment, such as a virtual machine or container, based on the predicted domain. This environment is configured with tools, libraries, and frameworks tailored to the application's domain, improving development efficiency and reducing setup time. The prediction model is trained on historical data to recognize patterns in application features that correlate with specific domains, allowing for accurate predictions even in early development stages. The system may further include a feedback mechanism to refine the model over time as new applications are developed and categorized. This approach streamlines the development process by automating domain identification and environment configuration, reducing manual effort and potential errors.
12. The system of claim 8 , wherein the executable operations further include generating a training corpus for training the classification model by extracting workflow semantics and corresponding UI modalities of the application from each of a plurality of platform-specific versions of previously developed applications.
The system relates to software development, specifically improving the automation of user interface (UI) generation for applications across different platforms. The problem addressed is the difficulty in creating consistent and platform-specific UIs for applications, which often requires manual adaptation of UI elements and workflows to different operating systems or devices. This process is time-consuming and error-prone, leading to inconsistencies in user experience. The system includes a classification model trained to generate UI elements and workflows for new applications based on learned patterns from existing applications. The model is trained using a training corpus derived from multiple platform-specific versions of previously developed applications. The training process involves extracting workflow semantics—such as user interaction sequences, data flows, and functional logic—and corresponding UI modalities—such as buttons, menus, and input fields—from these existing applications. By analyzing how these elements vary across platforms, the model learns to generate optimized UI designs that adhere to platform-specific guidelines while maintaining functional consistency. This approach reduces the need for manual UI adaptation, improving development efficiency and ensuring a more uniform user experience across different platforms. The system may also include additional features, such as real-time UI adjustments based on user feedback or dynamic platform updates.
13. The system of claim 8 , wherein the executable operations further include generating the plurality of alternative workflow segments by clustering semantically similar workflow segments with a classification model trained using machine learning to cluster the workflow segments based on a predetermined distance metric.
This invention relates to a system for optimizing workflows in a computing environment. The problem addressed is the inefficiency in workflow execution due to the lack of automated identification and clustering of semantically similar workflow segments, which can lead to redundant processing and suboptimal resource utilization. The system includes a processor and memory storing executable operations. These operations involve analyzing workflow segments to identify patterns and similarities. The system generates multiple alternative workflow segments by clustering semantically similar segments using a classification model. This model is trained with machine learning techniques to group workflow segments based on a predetermined distance metric, such as cosine similarity or Euclidean distance. The clustering process helps in identifying reusable or interchangeable segments, improving workflow efficiency and reducing redundancy. The system may also include a user interface for displaying the clustered workflow segments and allowing users to select or modify them. The executable operations further enable the system to execute the optimized workflows, ensuring that similar tasks are processed in a consistent and efficient manner. The machine learning model is continuously updated to improve clustering accuracy over time. This approach enhances workflow automation, reduces manual intervention, and optimizes resource allocation in computing environments.
14. A computer program product, comprising: a computer readable storage medium having program code stored thereon, the program code executable by a computer to initiate operations including: identifying a first workflow segment corresponding to a user-interface (UI) modality of an application, wherein the application is developed to run on a predetermined data processing platform; selecting at least one other workflow segment to transform the UI modality of the application, wherein the at least one other workflow segment is configured to perform, when executed on a different predetermined data processing platform, a function comparable to a function performable by the first workflow segment; and transforming, using the selected at least one other workflow segment, the UI modality of the application to perform, when executed on the different predetermined data processing platform, the function comparable to the function performable by the first workflow segment; wherein the at least one other workflow segment is selected from a plurality of alternative workflow segments that are semantically similar to the first workflow segment, and wherein the selecting is based on classifying the first workflow segment with a classification model trained using machine learning to map workflow segments and corresponding UI modalities of the application to different predetermined data processing platforms.
This invention relates to adapting user-interface (UI) modalities of applications across different data processing platforms. The problem addressed is the difficulty in maintaining consistent UI functionality when migrating applications between platforms with varying capabilities. The solution involves a computer program that identifies a workflow segment associated with a UI modality in an application designed for a specific platform. It then selects an alternative workflow segment from a pool of semantically similar options, where the alternative segment performs a comparable function on a different platform. The selection is based on a machine learning-trained classification model that maps workflow segments and their UI modalities to different platforms. The program then transforms the original UI modality using the selected segment, ensuring the application retains its functionality when executed on the new platform. The approach leverages semantic similarity and machine learning to automate UI adaptation, reducing manual effort and improving cross-platform compatibility.
15. The computer program product of claim 14 , wherein the operations further include generating a rank-ordered list of recommended workflow segments in response to selecting the at least one other workflow segment comprising a plurality of other workflow segments.
This invention relates to workflow management systems that recommend workflow segments to users. The problem addressed is the inefficiency in identifying and selecting relevant workflow segments from a large set of available options, which can slow down productivity and decision-making. The solution involves a computer program product that analyzes workflow segments and generates a rank-ordered list of recommended segments based on user selections. When a user selects at least one workflow segment, the system identifies a plurality of other workflow segments and ranks them according to relevance or suitability. The ranking may be based on factors such as historical usage, user preferences, or contextual data. The system dynamically updates the recommendations as new segments are selected, ensuring that the most relevant options are presented to the user. This improves workflow efficiency by reducing the time spent searching for appropriate segments and enhancing the overall user experience. The invention is particularly useful in environments where workflows are complex and involve multiple interdependent tasks, such as project management, software development, or business process automation.
16. The computer program product of claim 14 , wherein the operations further include transforming the UI modality of the application by automatically substituting a second code segment for a first code segment, wherein the first code segment corresponds to the first workflow segment, and wherein the second code segment corresponds to the at least one other workflow segment and performs, when executed on the different predetermined data processing platform, the function comparable to the function performable by the first workflow segment.
This invention relates to adapting software applications for different data processing platforms by transforming user interface (UI) modalities. The problem addressed is the difficulty of porting applications across platforms with varying UI capabilities, such as mobile devices, desktops, or embedded systems, without manual recoding. The solution involves automatically substituting code segments to modify the application's UI modality while preserving functionality. Specifically, a first code segment associated with a workflow segment is replaced with a second code segment corresponding to an alternative workflow segment. The second code segment performs the same function as the original but is optimized for the target platform. This transformation ensures compatibility without requiring developers to rewrite the entire application. The approach leverages predefined workflow segments and their equivalents, enabling seamless adaptation to different platforms while maintaining the application's core functionality. The invention simplifies cross-platform development by automating UI modality adjustments, reducing development time and effort.
17. The computer program product of claim 14 , wherein the operations further include predicting a domain of the application during an in-development phase using a prediction model trained with machine learning to predict the domain of the application based on a predetermined set of application features.
This invention relates to software development tools that predict the domain of an application during its development phase. The problem addressed is the lack of automated domain classification for applications under development, which can hinder targeted optimization, security measures, and feature recommendations. The solution involves a machine learning-based prediction model trained to classify an application's domain based on a predefined set of application features. These features may include code structure, libraries used, API calls, or other technical characteristics. The model analyzes these features to predict the application's domain, such as e-commerce, healthcare, or finance, even before the application is fully developed. This early prediction enables developers to apply domain-specific best practices, security protocols, or performance optimizations tailored to the predicted domain. The system may also refine predictions as more features become available during development. The invention improves efficiency by automating domain classification, reducing manual effort, and ensuring domain-appropriate development practices are applied early in the process.
18. The computer program product of claim 14 , wherein the operations further include generating a training corpus for training the classification model by extracting workflow semantics and corresponding UI modalities of the application from each of a plurality of platform-specific versions of previously developed applications.
This invention relates to a computer program product for improving application development by leveraging workflow semantics and user interface (UI) modalities from existing applications. The technology addresses the challenge of efficiently creating new applications by automating the extraction of reusable design patterns and interaction models from previously developed software. The system analyzes multiple platform-specific versions of existing applications to identify common workflow structures and UI elements, then compiles this data into a training corpus. This corpus is used to train a classification model that can predict optimal UI modalities and workflow semantics for new applications based on the learned patterns. The approach reduces development time and ensures consistency across different platforms by reusing proven design elements. The invention focuses on automating the extraction and application of best practices from prior implementations, enabling developers to build more intuitive and standardized applications. The system enhances productivity by minimizing redundant design work and improving the quality of new applications through data-driven insights.
19. The computer program product of claim 14 , wherein the operations further include generating the plurality of alternative workflow segments by clustering semantically similar workflow segments with a classification model trained using machine learning to cluster the workflow segments based on a predetermined distance metric.
This invention relates to computer program products for optimizing workflow automation by generating and selecting alternative workflow segments. The technology addresses the challenge of efficiently managing and automating complex workflows, particularly in environments where workflows must adapt to varying conditions or user preferences. The invention improves upon prior systems by dynamically generating multiple alternative workflow segments that can be selected based on contextual or performance criteria. The system uses machine learning to cluster semantically similar workflow segments into groups. A classification model, trained with machine learning techniques, analyzes workflow segments and groups them based on a predetermined distance metric, such as semantic similarity or functional equivalence. This clustering process ensures that alternative workflow segments are not only diverse but also logically related, improving the system's ability to suggest relevant alternatives. The generated segments can then be evaluated and selected based on factors like efficiency, resource usage, or user-defined priorities, allowing for adaptive and optimized workflow execution. The invention enhances automation systems by reducing manual intervention and improving decision-making in workflow management.
20. The computer program product of claim 14 , wherein the operations further include determining the corresponding UI modalities of the application based on accessibility text of the plurality of alternative workflow segments.
This invention relates to computer program products for enhancing accessibility in software applications by dynamically adapting user interface (UI) modalities based on accessibility text. The problem addressed is the lack of flexibility in traditional UI designs, which often fail to accommodate diverse user needs, such as those with visual, motor, or cognitive impairments. The solution involves analyzing accessibility text associated with alternative workflow segments within an application to determine the most suitable UI modalities for each user. These modalities may include visual, auditory, tactile, or other interactive elements tailored to the user's requirements. By dynamically adjusting the UI based on this analysis, the system ensures that users with different accessibility needs can interact with the application effectively. The invention also includes methods for generating and updating these UI modalities in real-time, ensuring seamless adaptation to changing user preferences or environmental conditions. This approach improves inclusivity and usability, making software applications more accessible to a broader audience.
Unknown
January 5, 2021
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